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deep learning

  • Q. Top 20 Deep Learning Interview Questions with detailed Answers (All free)
  • Q. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
  • Q. Explain the Transformer Architecture (with Examples and Videos)
  • Q. Why is Zero-centered output preferred for an activation function?
  • Q. Explain the Vanishing and Exploding Gradient Problems in Deep Learning
  • Q. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
  • Q. What is an activation function? What are the different types of activation functions? Discuss their pros and cons
  • Q. What are the key hyper-parameters of a neural network model?
  • Q. Describe briefly the training process of a Neural Network model
  • Q. What are some options for making Backpropagation more efficient?
  • Q. What are the advantages and disadvantages of Deep Learning?
  • Q. How does Deep Learning methods compare with traditional Machine Learning methods?
  • Q. How does Machine Learning differ from Classical Statistics and Deep Learning?
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      • Transformers (11)
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Other Questions in deep learning
  • Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
  • What is Bagging? How do you perform bagging and what are its advantages?
  • Explain the concept and working of the Random Forest model
  • What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
  • What are the advantages and disadvantages of Decision Tree model? 
  • What are the advantages and disadvantages of Random Forest?
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